IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v7y2019i6p584-598n6.html
   My bibliography  Save this article

General Method of Building a Real-Time Optimization Policy for Dynamic Vehicle Routing Problem

Author

Listed:
  • Xiong Hao

    (Management School, Hainan University, Haikou570228, China)

  • Yan Huili

    (Tourism School, Hainan University, Haikou570228, China)

Abstract

Currently, most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand. Here, a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed. Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones. The static sub-problems’ transformation and solution rules include: Division rule, batch rule, objective rule, action rule and algorithm rule, and so on. Different combinations of these rules may constitute a variety of real-time optimization policy. According to this general method, two new policies called flexible G/G/m and flexible D/G/m were developed. The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.

Suggested Citation

  • Xiong Hao & Yan Huili, 2019. "General Method of Building a Real-Time Optimization Policy for Dynamic Vehicle Routing Problem," Journal of Systems Science and Information, De Gruyter, vol. 7(6), pages 584-598, December.
    • Handle: RePEc:bpj:jossai:v:7:y:2019:i:6:p:584-598:n:6
    DOI: 10.21078/JSSI-2019-584-15
    as

    Download full text from publisher

    File URL: https://doi.org/10.21078/JSSI-2019-584-15
    Download Restriction: no
    File URL: https://libkey.io/10.21078/JSSI-2019-584-15?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michel Gendreau & François Guertin & Jean-Yves Potvin & Éric Taillard, 1999. "Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching," Transportation Science, INFORMS, vol. 33(4), pages 381-390, November.
    2. Dimitris J. Bertsimas & Garrett van Ryzin, 1991. "A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane," Operations Research, INFORMS, vol. 39(4), pages 601-615, August.
    3. Bertsimas, Dimitris & Van Ryzin, Garrett., 1991. "A stochastic and dynamic vehicle routing problem in the Euclidean plane," Working papers 3286-91., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    5. Dimitris J. Bertsimas & Garrett van Ryzin, 1993. "Stochastic and Dynamic Vehicle Routing in the Euclidean Plane with Multiple Capacitated Vehicles," Operations Research, INFORMS, vol. 41(1), pages 60-76, February.
    6. Zhi-Long Chen & Hang Xu, 2006. "Dynamic Column Generation for Dynamic Vehicle Routing with Time Windows," Transportation Science, INFORMS, vol. 40(1), pages 74-88, February.
    7. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2000. "Diversion Issues in Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 34(4), pages 426-438, November.
    8. Russell W. Bent & Pascal Van Hentenryck, 2004. "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers," Operations Research, INFORMS, vol. 52(6), pages 977-987, December.
    9. Papastavrou, Jason D., 1996. "A stochastic and dynamic routing policy using branching processes with state dependent immigration," European Journal of Operational Research, Elsevier, vol. 95(1), pages 167-177, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soeffker, Ninja & Ulmer, Marlin W. & Mattfeld, Dirk C., 2022. "Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review," European Journal of Operational Research, Elsevier, vol. 298(3), pages 801-820.
    2. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    3. Barrett W. Thomas, 2007. "Waiting Strategies for Anticipating Service Requests from Known Customer Locations," Transportation Science, INFORMS, vol. 41(3), pages 319-331, August.
    4. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    5. Marlin W. Ulmer & Justin C. Goodson & Dirk C. Mattfeld & Marco Hennig, 2019. "Offline–Online Approximate Dynamic Programming for Dynamic Vehicle Routing with Stochastic Requests," Service Science, INFORMS, vol. 53(1), pages 185-202, February.
    6. Jürgen Branke & Martin Middendorf & Guntram Noeth & Maged Dessouky, 2005. "Waiting Strategies for Dynamic Vehicle Routing," Transportation Science, INFORMS, vol. 39(3), pages 298-312, August.
    7. Ghiani, Gianpaolo & Guerriero, Francesca & Laporte, Gilbert & Musmanno, Roberto, 2003. "Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies," European Journal of Operational Research, Elsevier, vol. 151(1), pages 1-11, November.
    8. Chen, Lichun & Miller-Hooks, Elise, 2012. "Optimal team deployment in urban search and rescue," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 984-999.
    9. Marlin W. Ulmer & Dirk C. Mattfeld & Felix Köster, 2018. "Budgeting Time for Dynamic Vehicle Routing with Stochastic Customer Requests," Transportation Science, INFORMS, vol. 52(1), pages 20-37, January.
    10. Nikola Mardešić & Tomislav Erdelić & Tonči Carić & Marko Đurasević, 2023. "Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment," Mathematics, MDPI, vol. 12(1), pages 1-44, December.
    11. Zhang, Jian & Luo, Kelin & Florio, Alexandre M. & Van Woensel, Tom, 2023. "Solving large-scale dynamic vehicle routing problems with stochastic requests," European Journal of Operational Research, Elsevier, vol. 306(2), pages 596-614.
    12. Lars M. Hvattum & Arne Løkketangen & Gilbert Laporte, 2006. "Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic," Transportation Science, INFORMS, vol. 40(4), pages 421-438, November.
    13. Barrett W. Thomas & Chelsea C. White, 2004. "Anticipatory Route Selection," Transportation Science, INFORMS, vol. 38(4), pages 473-487, November.
    14. Cristián E. Cortés & Doris Sáez & Alfredo Núñez & Diego Muñoz-Carpintero, 2009. "Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 43(1), pages 27-42, February.
    15. Marlin W. Ulmer & Leonard Heilig & Stefan Voß, 2017. "On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 161-171, June.
    16. Jian Yang & Patrick Jaillet & Hani Mahmassani, 2004. "Real-Time Multivehicle Truckload Pickup and Delivery Problems," Transportation Science, INFORMS, vol. 38(2), pages 135-148, May.
    17. van de Klundert, J. & Wormer, L., 2008. "ASAP: the after salesman problem," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    18. Russell W. Bent & Pascal Van Hentenryck, 2004. "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers," Operations Research, INFORMS, vol. 52(6), pages 977-987, December.
    19. Diego Muñoz-Carpintero & Doris Sáez & Cristián E. Cortés & Alfredo Núñez, 2015. "A Methodology Based on Evolutionary Algorithms to Solve a Dynamic Pickup and Delivery Problem Under a Hybrid Predictive Control Approach," Transportation Science, INFORMS, vol. 49(2), pages 239-253, May.
    20. Joris van de Klundert & Laurens Wormer, 2010. "ASAP: The After-Salesman Problem," Manufacturing & Service Operations Management, INFORMS, vol. 12(4), pages 627-641, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jossai:v:7:y:2019:i:6:p:584-598:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.